Classifying Small Lesions on Breast MRI through Dynamic Enhancement Pattern Characterization
Dynamic characterization of the lesion enhancement pattern can improve the classification performance of small diagnostically challenging lesions on dynamic-contrast enhanced MRI. This involves extraction of texture features from all post-contrast images of the lesion rather than using the first post-contrast image alone. In this study, statistical texture features derived from gray-level co-occurrence matrices are extracted from all five post-contrast images of 60 lesions and then used in a supervised learning task with a support vector regressor. Our results show that this approach significantly improves the performance of classifying small lesions (p < 0.05). This suggests that such dynamic characterization of lesion enhancement has significant potential in assisting breast cancer diagnosis for small lesions.
Keywordsdynamic breast MRI texture analysis dynamic enhancement characterization gray-level co-occurence matrix support vector regression
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- 2.Schlossbauer, T., Leinsinger, G., Wismüller, A., Lange, O., Scherr, M., Meyer-Baese, A., Reiser, M.: Classification of small contrast enhancing breast lesions in dynamic magnetic resonance imaging using a combination of morphological criteria and dynamic analysis based on unsupervised vector-quantization. Investigative Radiology 43(1), 56–64 (2008)CrossRefGoogle Scholar
- 3.Leinsinger, G., Schlossbauer, T., Scherr, M., Lange, O., Reiser, M., Wismüller, A.: Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions? European Radiology 16(5), 1138–1146 (2006)CrossRefGoogle Scholar
- 4.Lerski, R.A., Straughan, K., Schad, L.R., Boyce, D., Blüml, S., Zuna, I.: Tissue characterization by magnetic-resonance spectroscopy and imaging - results of a concerted research-project of the european-economic-community.8. MR image texture analysis - an approach to tissue characterization. Magnetic Resonance Imaging 11(6), 873–887 (1993)CrossRefGoogle Scholar
- 9.Wilhjelm, J.E., Gronholdt, M.L.M., Wiebe, B., Jespersen, S.K., Hansen, L.K., Sillesen, H.: Quantitative analysis of ultrasound B-mode images of carotid atherosclerotic plaque: Correlation with visual classification and histological examination. IEEE Transactions on Medical Imaging 17(6), 910–922 (1998)CrossRefGoogle Scholar
- 13.Drucker, H., Burges, C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, vol. 9, pp. 155–161 (1996)Google Scholar